Consent-driven data use in crowdsensing platforms: When data reuse meets privacy-preservation

International audienceCrowdsensing is an essential element of the IoT; it allows gathering massive data across time and space to feed our environmental knowledge, and to link such knowledge to user behavior. However, there are major obstacles to crowdsensing, including the preservation of privacy. The consideration of privacy in crowdsensing systems has led to two main approaches, sometimes combined, which are, respectively, to trade privacy for rewards, and to take advantage of privacy-enhancing technologies "anonymizing" the collected data. Although relevant, we claim that these approaches do not sufficiently take into account the users' own tolerance to the use of the data provided, so that the crowdsensing system guarantees users the expected level of confidentiality as well as fosters the use of crowdsensing data for different tasks. To this end, we introduce the-completeness property, which ensures that the data provided can be used for all the tasks to which their owners consent as long as they are analyzed with − 1 other sources, and that no privacy violations can occur due to the related contribution of users with less stringent privacy requirements. The challenge, therefore, is to ensure-completeness when analyzing the data while allowing the data to be used for as many tasks as possible and promoting the accuracy of the resulting knowledge. We address this challenge with a clustering algorithm sensitive to the data distribution, which is shown to optimize data reuse and utility using a dataset from a deployed crowdsensing application

Big data y privacidad. Estudio bibliométrico

Revisión bibliográfica sobre la Privacidad de los datos personales en la actividad relacionada con el concepto “Big Data”.Universidad de Sevilla. Máster Universitario en Estudios Avanzados en Dirección de Empresa

Privacy and Confidentiality in Service Science and Big Data Analytics

Part 1: Invited Keynote PapersInternational audienceVast amounts of data are now being collected from census and surveys, scientific research, instruments, observation of consumer and internet activities, and sensors of many kinds. These data hold a wealth of information, however there is a risk that personal privacy will not be protected when they are accessed and used.This paper provides an overview of current and emerging approaches to balancing use and analysis of data with confidentiality protection in the research use of data, where the need for privacy protection is widely-recognised. These approaches were generally developed in the context of national statistical agencies and other data custodians releasing social and survey data for research, but are increasingly being adapted in the context of the globalisation of our information society. As examples, the paper contributes to a discussion of some of the issues regarding confidentiality in the service science and big data analytics contexts